3D angle of arrival capability in electronic devices with adaptability via memory augmentation

ABSTRACT

A method includes obtaining signal information based on wireless signals received from a target electronic device via a first antenna pair and a second antenna pair. The first and second antenna pairs are aligned along different axes. The signal information includes channel information, range information, a first angle of arrival (AoA) based on the first antenna pair, and a second AoA based on the second antenna pair. The method also includes obtaining tagging information that identifies an environment in which the electronic device is located. The method also includes generating encoded information from a memory module based on the tagging information. The method further includes initializing a field of view (FoV) classifier based on the encoded information. Additionally, the method includes determining whether the target electronic device is in a FoV of the electronic device based on the FoV classifier operating on the signal information.

CROSS-REFERENCE TO RELATED APPLICATIONS AND CLAIM OF PRIORITY

The present application claims priority to U.S. Provisional PatentApplication No. 63/134,366 filed on Jan. 6, 2021. The content of theabove-identified patent document is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to localizing an electronicdevice. More specifically, this disclosure relates to three-dimensionalangle of arrival capabilities in electronic devices.

BACKGROUND

The use of mobile computing technology has greatly expanded largely dueto usability, convenience, computing power, and the like. One result ofthe recent technological development is that electronic devices arebecoming more compact, while the number of functions and features that agiven device can perform is increasing. Certain electronic devices candetermine whether another device is within its field of view. Forexample, an electronic device can transmit and receive signals withother devices and determine an angle of arrival (AoA) of the receivedsignals and a distance between the devices. The signals can be corruptedwhich can create inaccurate AoA and range determinations. Inaccurate AoAand range determinations, can cause the electronic device to incorrectlydetermine that another electronic device is within its field of view oroutside its field of view.

Ultra-wideband (UWB) is a radio technology that employs a low energytransmission with a high bandwidth over a large portion a radiospectrum. The low energy transmission is performed over short-range atthe high bandwidth, such as over five-hundred mega-hertz (MHz). UWBapplications include sensor data collection, positional location, andtracking applications.

SUMMARY

Embodiments of the present disclosure provide methods and apparatusesfor three-dimensional angle of arrival capability in electronic devices.

In one embodiment, an electronic device is provided. The electronicdevice includes a processor. The processor is configured to obtainsignal information based on wireless signals received from a targetelectronic device via a first antenna pair and a second antenna pair.The first and second antenna pairs are aligned along different axes. Thesignal information includes channel information, range information, afirst AoA of the wireless signals based on the first antenna pair, and asecond AoA of the wireless signals based on the second antenna pair. Theprocessor is also configured to obtain tagging information thatidentifies an environment in which the electronic device is located. Theprocessor is also configured to generate encoded information from amemory module based on the tagging information. The processor is furtherconfigured to initialize a field of view (FoV) classifier based on theencoded information. Additionally, the processor is configured todetermine whether the target device is in a FoV of the electronic devicebased on the FoV classifier operating on the signal information.

In another embodiment, a method for operating an electronic device isprovided. The method includes obtaining signal information based onwireless signals received from a target electronic device via a firstantenna pair and a second antenna pair. The first and second antennapairs are aligned along different axes. The signal information includeschannel information, range information, a first AoA of the wirelesssignals based on the first antenna pair, and a second AoA of thewireless signals based on the second antenna pair. The method alsoincludes obtaining tagging information that identifies an environment inwhich the electronic device is located. The method also includesgenerating encoded information from a memory module based on the tagginginformation. The method further includes initializing a field of view(FoV) classifier based on the encoded information. Additionally, themethod includes determining whether the target device is in a FoV of theelectronic device based on the FoV classifier operating on the signalinformation.

In yet another embodiment a non-transitory computer readable mediumcontaining instructions is provided. The instructions that when executedcause a processor to obtain signal information based on wireless signalsreceived from a target electronic device via a first antenna pair and asecond antenna pair. The first and second antenna pairs are alignedalong different axes. The signal information includes channelinformation, range information, a first AoA of the wireless signalsbased on the first antenna pair, and a second AoA of the wirelesssignals based on the second antenna pair. The instructions that whenexecuted also cause the processor to obtain tagging information thatidentifies an environment in which the electronic device is located. Theinstructions that when executed also cause the processor to generateencoded information from a memory module based on the tagginginformation. The instructions that when executed further cause theprocessor to initialize a field of view (FoV) classifier based on theencoded information. Additionally, the instructions that when executedcause the processor to determine whether the target device is in a FoVof the electronic device based on the FoV classifier operating on thesignal information.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

Before undertaking the DETAILED DESCRIPTION below, it may beadvantageous to set forth definitions of certain words and phrases usedthroughout this patent document. The term “couple” and its derivativesrefer to any direct or indirect communication between two or moreelements, whether or not those elements are in physical contact with oneanother. The terms “transmit,” “receive,” and “communicate,” as well asderivatives thereof, encompass both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,means to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The term “controller” means any device, system or part thereofthat controls at least one operation. Such a controller may beimplemented in hardware or a combination of hardware and software and/orfirmware. The functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely. Thephrase “at least one of,” when used with a list of items, means thatdifferent combinations of one or more of the listed items may be used,and only one item in the list may be needed. For example, “at least oneof: A, B, and C” includes any of the following combinations: A, B, C, Aand B, A and C, B and C, and A and B and C.

Moreover, various functions described below can be implemented orsupported by one or more computer programs, each of which is formed fromcomputer readable program code and embodied in a computer readablemedium. The terms “application” and “program” refer to one or morecomputer programs, software components, sets of instructions,procedures, functions, objects, classes, instances, related data, or aportion thereof adapted for implementation in a suitable computerreadable program code. The phrase “computer readable program code”includes any type of computer code, including source code, object code,and executable code. The phrase “computer readable medium” includes anytype of medium capable of being accessed by a computer, such as readonly memory (ROM), random access memory (RAM), a hard disk drive, acompact disc (CD), a digital video disc (DVD), or any other type ofmemory. A “non-transitory” computer readable medium excludes wired,wireless, optical, or other communication links that transporttransitory electrical or other signals. A non-transitory computerreadable medium includes media where data can be permanently stored andmedia where data can be stored and later overwritten, such as arewritable optical disc or an erasable memory device.

Definitions for other certain words and phrases are provided throughoutthis patent document. Those of ordinary skill in the art shouldunderstand that in many if not most instances, such definitions apply toprior as well as future uses of such defined words and phrases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and itsadvantages, reference is now made to the following description taken inconjunction with the accompanying drawings, in which like referencenumerals represent like parts:

FIG. 1 illustrates an example communication system according toembodiments of the present disclosure;

FIG. 2 illustrates an example electronic device according to embodimentsof the present disclosure;

FIG. 3 illustrates an example network configuration according toembodiments of the present disclosure;

FIG. 4A illustrates an example diagram of a determination of whethertarget device is within a field of view (FoV) of an electronic deviceaccording to embodiments of the present disclosure;

FIG. 4B illustrates a diagram of an electronic device identifying angleof arrival (AoA) measurements of signals from an external electronicdevice according to embodiments of the present disclosure;

FIG. 4C illustrates a diagram of example antenna placements according toembodiments of the present disclosure;

FIG. 4D illustrates an example coordinate system according toembodiments of the present disclosure;

FIG. 4E illustrates an example diagram of an electronic devicedetermining that an external electronic device is within an azimuth FoVand an elevation FoV according to embodiments of the present disclosure;

FIGS. 5A, 5B, and 5C illustrate signal processing diagrams for field ofview determination according to embodiments of the present disclosure;

FIG. 6 illustrates an example post processor according to embodiments ofthe present disclosure;

FIG. 7 illustrates example channel impulse response (CIR) graphs for aninitial FoV determination according to embodiments of the presentdisclosure;

FIGS. 8A, 8B, and 8C illustrate an example classifier for determiningwhether an external electronic device is in the FoV of an electronicdevice according to embodiments of the present disclosure;

FIG. 9 illustrates an augmented memory module architecture according toembodiments of the present disclosure;

FIG. 10 illustrates an example memory state diagram according toembodiments of the present disclosure;

FIGS. 11 and 12 illustrate example processes for a memory processor anda memory retriever according to embodiments of the present disclosure;

FIG. 13 illustrates example method of scenario identification forselecting a classifier for an initial FoV determination according toembodiments of the present disclosure; and

FIG. 14 illustrates an example method for FoV determination based on newor existing environment according to embodiments of the presentdisclosure.

DETAILED DESCRIPTION

FIG. 1 through FIG. 14 , discussed below, and the various embodimentsused to describe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any suitably arranged system or device.

Aspects, features, and advantages of the disclosure are readily apparentfrom the following detailed description, simply by illustrating a numberof particular embodiments and implementations, including the best modecontemplated for carrying out the disclosure. The disclosure is alsocapable of other and different embodiments, and its several details canbe modified in various obvious respects, all without departing from thespirit and scope of the disclosure. Accordingly, the drawings anddescription are to be regarded as illustrative in nature, and not asrestrictive. The disclosure is illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings.

An electronic device, according to embodiments of the presentdisclosure, can include a personal computer (such as a laptop, adesktop), a workstation, a server, a television, an appliance, and thelike. In certain embodiments, an electronic device can be a portableelectronic device such as a portable communication device (such as asmartphone or mobile phone), a laptop, a tablet, an electronic bookreader (such as an e-reader), a personal digital assistants (PDAs), aportable multimedia player (PMP), an MP3 player, a mobile medicaldevice, a virtual reality headset, a portable game console, a camera,and a wearable device, among others. Additionally, the electronic devicecan be at least one of a part of a piece of furniture orbuilding/structure, an electronic board, an electronic signaturereceiving device, a projector, or a measurement device. The electronicdevice is one or a combination of the above-listed devices.Additionally, the electronic device as disclosed herein is not limitedto the above-listed devices and can include new electronic devicesdepending on the development of technology. It is noted that as usedherein, the term “user” may denote a human or another device (such as anartificial intelligent electronic device) using the electronic device.

Certain embodiments of the present disclosure provide a system thatincludes a device and one or multiple targets. The device and target(s)are both equipped with wireless transmitters, receivers, transceivers,or sensors. Similarly, the transmitter (or transceiver) target devicescan be a UWB transmitter (or UWB transceiver). The device, or both thedevice and the target(s), are capable of measuring angle of arrival(AoA) in azimuth (AoA_az) and elevation (AoA_el) based on the signaltransmitted by the transmitter of the target or a transmitter of adevice. One example of such transceiver or sensors is an ultra wideband(UWB) transceiver or sensor. Embodiments of the present disclosureaddress a problem of identifying if the target is within a certain 3Dfield of view (FoV), in terms of its location in azimuth and elevation,of the device using UWB technology. As used herein target device andexternal electronic device are used interchangeably and refer to adevice that the electronic device is attempting to locate.

Generally, UWB measurements between the device and target are impactedby impairments such as antenna and RF impairments and can also sufferfrom multipath effect. Without proper processing, it is difficult toidentify a location of the target. Specifically, environmental factorscan affect the field of view classification performance.

FoV can be any range of angles around the boresight within which thetarget device can be defined as identifiable or present. If there isdirect line of sight (LoS) between the electronic device and targetdevice, and range and angle of arrival (AoA) measurements can be used toidentify whether the presence of target device is in the FoV.

UWB signals provide centimeter level ranging. For example, if the targetdevice is within LoS of the electronic device, the electronic device candetermine the range (distance) between the two devices with an accuracythat is within ten centimeters. Alternatively if the target device isnot within a LoS of the electronic device, the electronic device candetermine the range between the two devices with an accuracy that iswithin fifty centimeters. Additionally, if the target device is withinLoS of the electronic device, the electronic device can determine theAoA between the two devices with an accuracy that is within threedegrees.

Certain embodiments of the present disclosure provide a system andmethod to more accurately detect if the target is present in athree-dimensional (3D) field of view (FoV) of the device. The 3D FoVdetection is useful to achieve a better user experience in applicationssuch as peer-to-peer file sharing, augmented reality (AR)/virtualreality (VR) applications, and the like. Certain embodiments provide asystem that has the capability to continuously learn and adapt to theenvironment. That is, embodiments of the present disclosure provide asystem and method that is robust toward environmental change.Additionally, certain embodiments of the present disclosure address theproblem in the context of identifying the FoV location using UWBtechnology, and addresses a problem that involves radar-based sensor.

To address this UWB impairment issue, embodiments of the presentdisclosure use an augmented memory module (AMM) that provides a FoVclassifier a capability to continuously learn and adapt to theenvironment. Existing UWB solutions do not use an AMM. The utilizationof the AMM is described as follows. When the 3D FOV system encounters anunknown environment, the 3D FOV classifier starts from a blank state,meaning there is no memory of this new environment. This will result inlower performance of the classifier at the beginning. The performancewill increase when the classifier becomes more familiar with theperformance by processing more inputs gathered from this environment. Atthis point the current long term memory cell state of the classifier istagged and stored in the AMM by a Memory Processor. Alternatively, whenthe system encounters a familiar environment, the encoded memorycorresponding to this environment is retrieved from the AMM by theMemory Retriever and subsequently provided to the 3D FOV classifier.Therefore, the classifier is not required to start from a blank stateand the performance in the beginning of the encounter is better.Additionally, the memory dedicated to this environment is updated fromthe additional encounter using the mechanism in the Memory Processor.This process provides the system a level of adaptability toward changesin the environment.

Certain embodiments of the present disclosure reduce the false positiverate (for example, a false indication of whether a target object lies inthe FOV) by 1.5×-2× compared to FOV detectors that do not use an AMM.The stability and variation of AoA output are also significantlyimproved, thereby improving the user experience in applications thatdetect or find objects, in peer-to-peer file sharing, augmented reality,or in mixed-reality devices.

The present disclosure first describes the problem of identifying thepresence of target in the 3D field of view (FoV) of the device. A targetis in 3D FoV if azimuth and elevation are both in specified FoV angleregions. Embodiments of the present disclosure include: 1) Methods touse measurements to predict if the target is in a certain 3D FoV of thedevice using a classifier; and 2) Methods to evolve the classifier sothat it can be highly adaptive and robust toward environmental changes.Certain embodiments of the present disclosure can be summarized asfollows. A first embodiment includes: obtaining tagging information thatidentifies an environment in which a device is located; generatingencoded information from a memory module based on the tagginginformation; initializing a classifier using the encoded information;and determining whether a target is in a FoV of the device based on theclassifier operating on ultrawide band (UWB) features and measurements.A second embodiment includes: in cases where the device has previouslyencountered the environment, the encoded information includes a currentlong term memory cell state of the classifier; and, in cases where thedevice has not previously encountered the environment, the encodedinformation includes a generic initial state of the classifier. A thirdembodiment further includes updating the encoded information associatedwith the tagging information after the determining step.

FIG. 1 illustrates an example communication system 100 in accordancewith an embodiment of this disclosure. The embodiment of thecommunication system 100 shown in FIG. 1 is for illustration only. Otherembodiments of the communication system 100 can be used withoutdeparting from the scope of this disclosure.

The communication system 100 includes a network 102 that facilitatescommunication between various components in the communication system100. For example, the network 102 can communicate IP packets, framerelay frames, Asynchronous Transfer Mode (ATM) cells, or otherinformation between network addresses. The network 102 includes one ormore local area networks (LANs), metropolitan area networks (MANs), widearea networks (WANs), all or a portion of a global network such as theInternet, or any other communication system or systems at one or morelocations.

In this example, the network 102 facilitates communications between aserver 104 and various client devices 106-114. The client devices106-114 may be, for example, a smartphone, a tablet computer, a laptop,a personal computer, a wearable device, a head mounted display, or thelike. The server 104 can represent one or more servers. Each server 104includes any suitable computing or processing device that can providecomputing services for one or more client devices, such as the clientdevices 106-114. Each server 104 could, for example, include one or moreprocessing devices, one or more memories storing instructions and data,and one or more network interfaces facilitating communication over thenetwork 102.

In certain embodiments, the server 104 is a neural network that isconfigured to extract features from the received signals, determinewhether a target device (such as one of the client devices 108-114 iswithin the field of view of another one of the client devices 108-114),or both. In certain embodiments, the neural network is included withinany of the client devices 106-114. When a neural network is included ina client device, the client device can use the neural network to extractfeatures from the received signals, without having to transmit contentover the network 102. Similarly, when a neural network is included in aclient device, the client device can use the neural network to identifywhether another client device is within the field of view of the clientdevice that includes the neural network.

Each of the client devices 106-114 represent any suitable computing orprocessing device that interacts with at least one server (such as theserver 104) or other computing device(s) over the network 102. Theclient devices 106-114 include a desktop computer 106, a mobiletelephone or mobile device 108 (such as a smartphone), a PDA 110, alaptop computer 112, and a tablet computer 114. However, any other oradditional client devices could be used in the communication system 100.Smartphones represent a class of mobile devices 108 that are handhelddevices with mobile operating systems and integrated mobile broadbandcellular network connections for voice, short message service (SMS), andInternet data communications. In certain embodiments, any of the clientdevices 106-114 can emit and collect radar signals via a measuringtransceiver. In certain embodiments, any of the client devices 106-114can emit and collect UWB signals via a measuring transceiver.

In this example, some client devices 108-114 communicate indirectly withthe network 102. For example, the mobile device 108 and PDA 110communicate via one or more base stations 116, such as cellular basestations or eNodeBs (eNBs). Also, the laptop computer 112 and the tabletcomputer 114 communicate via one or more wireless access points 118,such as IEEE 802.11 wireless access points. Note that these are forillustration only and that each of the client devices 106-114 couldcommunicate directly with the network 102 or indirectly with the network102 via any suitable intermediate device(s) or network(s). In certainembodiments, any of the client devices 106-114 transmit informationsecurely and efficiently to another device, such as, for example, theserver 104.

As illustrated, the laptop computer 112 can communicate with the mobiledevice 108. Based on the wireless signals which are communicated betweenthese two devices, a device (such as the laptop computer 112, the mobiledevice 108, or another device, such as the server 104) obtaining channelinformation, range information, and AoA information. Channel informationcan include features of a channel impulse response (CIR) of a wirelesschannel between the laptop computer 112 and the mobile device 108. Therange can be an instantaneous distance or variances in the distancesbetween the laptop computer 112 and the mobile device 108, based on thewireless signals. Similarly, the AoA can be an instantaneous AoAmeasurement or variances in AoA measurements between the laptop computer112 and the mobile device 108, based on the wireless signals.

Although FIG. 1 illustrates one example of a communication system 100,various changes can be made to FIG. 1 . For example, the communicationsystem 100 could include any number of each component in any suitablearrangement. In general, computing and communication systems come in awide variety of configurations, and FIG. 1 does not limit the scope ofthis disclosure to any particular configuration. While FIG. 1illustrates one operational environment in which various featuresdisclosed in this patent document can be used, these features could beused in any other suitable system.

FIG. 2 illustrates an example electronic device in accordance with anembodiment of this disclosure. More particularly, FIG. 2 illustrates anexample electronic device 200, and the electronic device 200 couldrepresent the server 104 or one or more of the client devices 106-114 inFIG. 1 . The electronic device 200 can be a mobile communication device,such as, for example, a mobile station, a subscriber station, a wirelessterminal, a desktop computer (similar to the desktop computer 106 ofFIG. 1 ), a portable electronic device (similar to the mobile device108, the PDA 110, the laptop computer 112, or the tablet computer 114 ofFIG. 1 ), a robot, and the like.

As shown in FIG. 2 , the electronic device 200 includes transceiver(s)210, transmit (TX) processing circuitry 215, a microphone 220, andreceive (RX) processing circuitry 225. The transceiver(s) 210 caninclude, for example, a RF transceiver, a BLUETOOTH transceiver, a WiFitransceiver, a ZIGBEE transceiver, an infrared transceiver, and variousother wireless communication signals. The electronic device 200 alsoincludes a speaker 230, a processor 240, an input/output (I/O) interface(IF) 245, an input 250, a display 255, a memory 260, and a sensor 265.The memory 260 includes an operating system (OS) 261, and one or moreapplications 262.

The transceiver(s) 210 can include an antenna array including numerousantennas. The antennas of the antenna array can include a radiatingelement composed of a conductive material or a conductive pattern formedin or on a substrate. The transceiver(s) 210 transmit and receive asignal or power to or from the electronic device 200. The transceiver(s)210 receives an incoming signal transmitted from an access point (suchas a base station, WiFi router, or BLUETOOTH device) or other device ofthe network 102 (such as a WiFi, BLUETOOTH, cellular, 5G, LTE, LTE-A,WiMAX, or any other type of wireless network). The transceiver(s) 210down-converts the incoming RF signal to generate an intermediatefrequency or baseband signal. The intermediate frequency or basebandsignal is sent to the RX processing circuitry 225 that generates aprocessed baseband signal by filtering, decoding, and/or digitizing thebaseband or intermediate frequency signal. The RX processing circuitry225 transmits the processed baseband signal to the speaker 230 (such asfor voice data) or to the processor 240 for further processing (such asfor web browsing data).

The TX processing circuitry 215 receives analog or digital voice datafrom the microphone 220 or other outgoing baseband data from theprocessor 240. The outgoing baseband data can include web data, e-mail,or interactive video game data. The TX processing circuitry 215 encodes,multiplexes, and/or digitizes the outgoing baseband data to generate aprocessed baseband or intermediate frequency signal. The transceiver(s)210 receives the outgoing processed baseband or intermediate frequencysignal from the TX processing circuitry 215 and up-converts the basebandor intermediate frequency signal to a signal that is transmitted.

The processor 240 can include one or more processors or other processingdevices. The processor 240 can execute instructions that are stored inthe memory 260, such as the OS 261 in order to control the overalloperation of the electronic device 200. For example, the processor 240could control the reception of forward channel signals and thetransmission of reverse channel signals by the transceiver(s) 210, theRX processing circuitry 225, and the TX processing circuitry 215 inaccordance with well-known principles. The processor 240 can include anysuitable number(s) and type(s) of processors or other devices in anysuitable arrangement. For example, in certain embodiments, the processor240 includes at least one microprocessor or microcontroller. Exampletypes of the processor 240 include microprocessors, microcontrollers,digital signal processors, field programmable gate arrays, applicationspecific integrated circuits, and discrete circuitry. In certainembodiments, the processor 240 includes a neural network.

The processor 240 is also capable of executing other processes andprograms resident in the memory 260, such as operations that receive andstore data. The processor 240 can move data into or out of the memory260 as required by an executing process. In certain embodiments, theprocessor 240 is configured to execute the one or more applications 262based on the OS 261 or in response to signals received from externalsource(s) or an operator. Example, applications 262 can include amultimedia player (such as a music player or a video player), a phonecalling application, a virtual personal assistant, and the like.

The processor 240 is also coupled to the I/O interface 245 that providesthe electronic device 200 with the ability to connect to other devices,such as client devices 106-114. The I/O interface 245 is thecommunication path between these accessories and the processor 240.

The processor 240 is also coupled to the input 250 and the display 255.The operator of the electronic device 200 can use the input 250 to enterdata or inputs into the electronic device 200. The input 250 can be akeyboard, touchscreen, mouse, track ball, voice input, or other devicecapable of acting as a user interface to allow a user in interact withthe electronic device 200. For example, the input 250 can include voicerecognition processing, thereby allowing a user to input a voicecommand. In another example, the input 250 can include a touch panel, a(digital) pen sensor, a key, or an ultrasonic input device. The touchpanel can recognize, for example, a touch input in at least one scheme,such as a capacitive scheme, a pressure sensitive scheme, an infraredscheme, or an ultrasonic scheme. The input 250 can be associated withthe sensor(s) 265, the measuring transceiver 270, a camera, and thelike, which provide additional inputs to the processor 240. The input250 can also include a control circuit. In the capacitive scheme, theinput 250 can recognize touch or proximity.

The display 255 can be a liquid crystal display (LCD), light-emittingdiode (LED) display, organic LED (OLED), active matrix OLED (AMOLED), orother display capable of rendering text and/or graphics, such as fromwebsites, videos, games, images, and the like. The display 255 can be asingular display screen or multiple display screens capable of creatinga stereoscopic display. In certain embodiments, the display 255 is aheads-up display (HUD).

The memory 260 is coupled to the processor 240. Part of the memory 260could include a RAM, and another part of the memory 260 could include aFlash memory or other ROM. The memory 260 can include persistent storage(not shown) that represents any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information). The memory 260 can contain one ormore components or devices supporting longer-term storage of data, suchas a read only memory, hard drive, Flash memory, or optical disc.

The electronic device 200 further includes one or more sensors 265 thatcan meter a physical quantity or detect an activation state of theelectronic device 200 and convert metered or detected information intoan electrical signal. For example, the sensor 265 can include one ormore buttons for touch input, a camera, a gesture sensor, opticalsensors, cameras, one or more inertial measurement units (IMUs), such asa gyroscope or gyro sensor, and an accelerometer. The sensor 265 canalso include an air pressure sensor, a magnetic sensor or magnetometer,a grip sensor, a proximity sensor, an ambient light sensor, abio-physical sensor, a temperature/humidity sensor, an illuminationsensor, an Ultraviolet (UV) sensor, an Electromyography (EMG) sensor, anElectroencephalogram (EEG) sensor, an Electrocardiogram (ECG) sensor, anIR sensor, an ultrasound sensor, an iris sensor, a fingerprint sensor, acolor sensor (such as a Red Green Blue (RGB) sensor), and the like. Thesensor 265 can further include control circuits for controlling any ofthe sensors included therein. Any of these sensor(s) 265 may be locatedwithin the electronic device 200 or within a secondary device operablyconnected to the electronic device 200.

In this embodiment, one of the one or more transceivers in thetransceiver 210 is the measuring transceiver 270. The measuringtransceiver 270 is configured to transmit and receive signals fordetecting and ranging purposes. The measuring transceiver 270 cantransmit and receive signals for measuring range and angle of anexternal object relative to the electronic device 200. The measuringtransceiver 270 may be any type of transceiver including, but notlimited to a WiFi transceiver, for example, an 802.11ay transceiver, aUWB transceiver, and the like. In certain embodiments, the measuringtransceiver 270 includes a sensor. For example, the measuringtransceiver 270 can operate both measuring and communication signalsconcurrently. The measuring transceiver 270 includes one or more antennaarrays, or antenna pairs, that each includes a transmitter (ortransmitter antenna) and a receiver (or receiver antenna). The measuringtransceiver 270 can transmit signals at various frequencies, such as inUWB. The measuring transceiver 270 can receive the signals from a targetdevice (such as an external electronic device) for determining whetherthe target device within the FoV of the electronic device 200.

The transmitter, of the measuring transceiver 270, can transmit UWBsignals. The receiver, of the measuring transceiver, can receive UWBsignals from other electronic devices. The processor 240 can analyze thetime difference, based on the time stamps of transmitted and receivedsignals, to measure the distance of the target objects from theelectronic device 200. Based on the time differences, the processor 240can generate location information, indicating a distance that the targetdevice is from the electronic device 200. In certain embodiments, themeasuring transceiver 270 is a sensor that can detect range and AoA ofanother electronic device. For example, the measuring transceiver 270can identify changes in azimuth and/or elevation of the other electronicdevice relative to the measuring transceiver 270. In certainembodiments, the measuring transceiver 270 represents two or moretransceivers. Based on the differences between a signal received by eachof the transceivers, the processor 240 can determine the identifychanges in azimuth and/or elevation corresponding to the AoA of thereceived signals.

In certain embodiments, the measuring transceiver 270 can include pairsof antennas for measuring AoA of the signals. Based on the orientationof the antenna pairs, the measuring transceiver 270 can determine AoA inazimuth and AoA in elevation. FIG. 4C, below, describes an exampleorientation of two antenna pairs sharing one antenna.

Although FIG. 2 illustrates one example of electronic device 200,various changes can be made to FIG. 2 . For example, various componentsin FIG. 2 can be combined, further subdivided, or omitted and additionalcomponents can be added according to particular needs. As a particularexample, the processor 240 can be divided into multiple processors, suchas one or more central processing units (CPUs), one or more graphicsprocessing units (GPUs), one or more neural networks, and the like.Also, while FIG. 2 illustrates the electronic device 200 configured as amobile telephone, tablet, or smartphone, the electronic device 200 canbe configured to operate as other types of mobile or stationary devices.

FIG. 3 illustrates an example network configuration according toembodiments of the present disclosure. An embodiment of the networkconfiguration shown in FIG. 3 is for illustration only. One or more ofthe components illustrated in FIG. 3 can be implemented in specializedcircuitry configured to perform the noted functions or one or more ofthe components can be implemented by one or more processors executinginstructions to perform the noted functions.

FIG. 3 illustrated a block diagram illustrating a network configurationincluding an electronic device 301 in a network environment 300according to various embodiments. As illustrated in FIG. 3 , theelectronic device 301 in the network environment 300 may communicatewith an electronic device 302 via a first network 398 (e.g., ashort-range wireless communication network), or an electronic device 304or a server 308 via a second network 399 (e.g., a long-range wirelesscommunication network). The first network 398 and/or the second network399 can be similar to the network 102 of FIG. 1 . The electronic devices301, 302, and 304 can be similar to any of the client devices 106-114 ofFIG. 1 and include similar components to that of the electronic device200 of FIG. 2 . The server 308 can be similar to the server 104 of FIG.1 .

The electronic device 301 can be one of various types of electronicdevices. The electronic devices may include, for example, a portablecommunication device (e.g., a smartphone), a computer device, a portablemultimedia device, a portable medical device, a camera, a wearabledevice, or a home appliance. According to an embodiment of thedisclosure, the electronic devices are not limited to those describedabove.

According to an embodiment, the electronic device 301 may communicatewith the electronic device 304 via the server 308. According to anembodiment, the electronic device 301 may include a processor 320,memory 330, an input device 350, a sound output device 355, a displaydevice 360, an audio module 370, a sensor module 376, an interface 377,a haptic module 379, a camera module 380, a power management module 388,a battery 389, a communication module 390, a subscriber identificationmodule (SIM) 396, or an antenna module 397. In some embodiments, atleast one (e.g., the display device 360 or the camera module 380) of thecomponents may be omitted from the electronic device 301, or one or moreother components may be added in the electronic device 301. In someembodiments, some of the components may be implemented as singleintegrated circuitry. For example, the sensor module 376 (e.g., afingerprint sensor, an iris sensor, or an illuminance sensor) may beimplemented as embedded in the display device 360 (e.g., a display).

The processor 320 may execute, for example, software (e.g., a program340) to control at least one other component (e.g., a hardware orsoftware component) of the electronic device 301 coupled with theprocessor 320 and may perform various data processing or computation.According to one embodiment, as at least part of the data processing orcomputation, the processor 320 may load a command or data received fromanother component (e.g., the sensor module 376 or the communicationmodule 390) in volatile memory 332, process the command or the datastored in the volatile memory 332, and store resulting data innon-volatile memory 334.

According to an embodiment, the processor 320 may include a mainprocessor 321 (e.g., a central processing unit (CPU) or an applicationprocessor (AP)), and an auxiliary processor 323 (e.g., a graphicsprocessing unit (GPU), an image signal processor (ISP), a sensor hubprocessor, or a communication processor (CP)) that is operableindependently from, or in conjunction with, the main processor 321.Additionally or alternatively, the auxiliary processor 323 may beadapted to consume less power than the main processor 321, or to bespecific to a specified function. The auxiliary processor 323 may beimplemented as separate from, or as part of the main processor 321.

The auxiliary processor 323 may control at least some of functions orstates related to at least one component (e.g., the display device 360,the sensor module 376, or the communication module 390) among thecomponents of the electronic device 301, instead of the main processor321 while the main processor 321 is in an inactive (e.g., sleep) state,or together with the main processor 321 while the main processor 321 isin an active state (e.g., executing an application). According to anembodiment, the auxiliary processor 323 (e.g., an image signal processoror a communication processor) may be implemented as part of anothercomponent (e.g., the camera module 380 or the communication module 390)functionally related to the auxiliary processor 323.

The memory 330 may store various data used by at least one component(e.g., the processor 320 or the sensor module 376) of the electronicdevice 301. The various data may include, for example, software (e.g.,the program 340) and input data or output data for a command relatedthereto. The memory 330 may include the volatile memory 332 or thenon-volatile memory 334.

The program 340 may be stored in the memory 330 as software. The program340 may include, for example, an operating system (OS) 342, middleware344, or an application 346.

The input device 350 may receive a command or data to be used by othercomponents (e.g., the processor 320) of the electronic device 301, fromthe outside (e.g., a user) of the electronic device 301. The inputdevice 350 may include, for example, a microphone, a mouse, a keyboard,or a digital pen (e.g., a stylus pen). In certain embodiments, the inputdevice 350 includes a sensor for gesture recognition. For example, theinput device 350 can include a transceiver similar to the measuringtransceiver 270 of FIG. 2 .

The sound output device 355 may output sound signals to the outside ofthe electronic device 301. The sound output device 355 may include, forexample, a speaker or a receiver. The speaker may be used for generalpurposes, such as playing multimedia or playing record, and the receivermay be used for incoming calls. According to an embodiment, the receivermay be implemented as separate from, or as part of the speaker.

The display device 360 may visually provide information to the outside(e.g., a user) of the electronic device 301. The display device 360 mayinclude, for example, a display, a hologram device, or a projector andcontrol circuitry to control a corresponding one of the display,hologram device, or projector. According to an embodiment, the displaydevice 360 may include touch circuitry adapted to detect a touch, orsensor circuitry (e.g., a pressure sensor) adapted to measure theintensity of force incurred by the touch. The display device 360 can besimilar to the display 255 of FIG. 2 .

The audio module 370 may convert a sound into an electrical signal andvice versa. According to an embodiment, the audio module 370 may obtainthe sound via the input device 350, output the sound via the soundoutput device 355, or output the sound via a headphone of an externalelectronic device (e.g., an electronic device 302) directly (e.g.,wiredly) or wirelessly coupled with the electronic device 301.

The sensor module 376 may detect an operational state (e.g., power ortemperature) of the electronic device 301 or an environmental state(e.g., a state of a user) external to the electronic device 301, andthen generate an electrical signal or data value corresponding to thedetected state. According to an embodiment, the sensor module 376 mayinclude, for example, a gesture sensor, a gyro sensor, an atmosphericpressure sensor, a magnetic sensor, an acceleration sensor, a gripsensor, a proximity sensor, a color sensor, an infrared (IR) sensor, abiometric sensor, a temperature sensor, a humidity sensor, or anilluminance sensor. The sensor module 376 can be similar to the sensors265 of FIG. 2 .

The interface 377 may support one or more specified protocols to be usedfor the electronic device 101 to be coupled with the external electronicdevice (e.g., the electronic device 302) directly (e.g., wiredly) orwirelessly. According to an embodiment, the interface 377 may include,for example, a high definition multimedia interface (HDMI), a universalserial bus (USB) interface, a secure digital (SD) card interface, or anaudio interface.

A connecting terminal 378 may include a connector via which theelectronic device 301 may be physically connected with the externalelectronic device (e.g., the electronic device 302). According to anembodiment, the connecting terminal 378 may include, for example, a HDMIconnector, a USB connector, a SD card connector, or an audio connector(e.g., a headphone connector).

The haptic module 379 may convert an electrical signal into a mechanicalstimulus (e.g., a vibration or a movement) or electrical stimulus whichmay be recognized by a user via his tactile sensation or kinestheticsensation. According to an embodiment, the haptic module 379 mayinclude, for example, a motor, a piezoelectric element, or an electricstimulator.

The camera module 380 may capture a still image or moving images.According to an embodiment, the camera module 380 may include one ormore lenses, image sensors, image signal processors, or flashes.

The power management module 388 may manage power supplied to theelectronic device 301. According to one embodiment, the power managementmodule 388 may be implemented as at least part of, for example, a powermanagement integrated circuit (PMIC).

The battery 389 may supply power to at least one component of theelectronic device 301. According to an embodiment, the battery 389 mayinclude, for example, a primary cell which is not rechargeable, asecondary cell which is rechargeable, or a fuel cell.

The communication module 390 may support establishing a direct (e.g.,wired) communication channel or a wireless communication channel betweenthe electronic device 301 and the external electronic device (e.g., theelectronic device 302, the electronic device 304, or the server 308) andperforming communication via the established communication channel. Thecommunication module 390 may include one or more communicationprocessors that are operable independently from the processor 320 (e.g.,the application processor (AP)) and supports a direct (e.g., wired)communication or a wireless communication.

According to an embodiment, the communication module 390 may include awireless communication module 392 (e.g., a cellular communicationmodule, a short-range wireless communication module, or a globalnavigation satellite system (GNSS) communication module) or a wiredcommunication module 394 (e.g., a local area network (LAN) communicationmodule or a power line communication (PLC) module). A corresponding oneof these communication modules may communicate with the externalelectronic device via the first network 398 (e.g., a short-rangecommunication network, such as BLUETOOTH, wireless-fidelity (Wi-Fi)direct, UWB, or infrared data association (IrDA)) or the second network399 (e.g., a long-range communication network, such as a cellularnetwork, the Internet, or a computer network (e.g., LAN or wide areanetwork (WAN))). These various types of communication modules may beimplemented as a single component (e.g., a single chip), or may beimplemented as multi components (e.g., multi chips) separate from eachother. The wireless communication module 392 may identify andauthenticate the electronic device 301 in a communication network, suchas the first network 398 or the second network 399, using subscriberinformation (e.g., international mobile subscriber identity (IMSI))stored in the subscriber identification module 396.

The antenna module 397 may transmit or receive a signal or power to orfrom the outside (e.g., the external electronic device) of theelectronic device 301. According to an embodiment, the antenna module397 may include an antenna including a radiating element composed of aconductive material or a conductive pattern formed in or on a substrate(e.g., PCB).

According to an embodiment, the antenna module 397 may include aplurality of antennas. In such a case, at least one antenna appropriatefor a communication scheme used in the communication network, such asthe first network 398 or the second network 399, may be selected, forexample, by the communication module 390 (e.g., the wirelesscommunication module 392) from the plurality of antennas. The signal orthe power may then be transmitted or received between the communicationmodule 390 and the external electronic device via the selected at leastone antenna.

According to an embodiment, another component (e.g., a radio frequencyintegrated circuit (RFIC)) other than the radiating element may beadditionally formed as part of the antenna module 397.

At least some of the above-described components may be coupled mutuallyand communicate signals (e.g., commands or data) therebetween via aninter-peripheral communication scheme (e.g., a bus, general purposeinput and output (GPIO), serial peripheral interface (SPI), or mobileindustry processor interface (MIPI)).

According to an embodiment, commands or data may be transmitted orreceived between the electronic device 301 and the external electronicdevice 304 via the server 308 coupled with the second network 399. Eachof the electronic devices 302 and 304 may be a device of a same type as,or a different type, from the electronic device 301. According to anembodiment, all or some of operations to be executed at the electronicdevice 301 may be executed at one or more of the external electronicdevices 302 or 304. For example, if the electronic device 301 mayperform a function or a service automatically, or in response to arequest from a user or another device, the electronic device 301,instead of, or in addition to, executing the function or the service,may request the one or more external electronic devices to perform atleast part of the function or the service.

The one or more external electronic devices receiving the request mayperform the at least part of the function or the service requested, oran additional function or an additional service related to the requestand transfer an outcome of the performing to the electronic device 301.The electronic device 301 may provide the outcome, with or withoutfurther processing of the outcome, as at least part of a reply to therequest. To that end, a cloud computing, distributed computing, orclient-server computing technology may be used, for example.

Although FIG. 3 illustrates one example of the electronic device 301 inthe network environment 300, various changes can be made to FIG. 3 . Forexample, various components in FIG. 3 can be combined, furthersubdivided, or omitted and additional components can be added accordingto particular needs. As a particular example, the processor 320 can befurther divided into additional processors, such as one or more centralprocessing units (CPUs), one or more graphics processing units (GPUs),one or more neural networks, and the like. Also, while FIG. 3illustrates the electronic device 301 configured as a mobile telephone,tablet, or smartphone, the electronic device 301 can be configured tooperate as other types of mobile or stationary devices.

FIG. 4A illustrates an example diagram 400 of a determination of whethertarget device is within a FoV of an electronic device according toembodiments of the present disclosure. FIG. 4B illustrates a diagram 420of an electronic device identifying AoA measurements of signals from anexternal electronic device according to embodiments of the presentdisclosure. FIG. 4C illustrates a diagram 430 of example antennaplacements according to embodiments of the present disclosure. FIG. 4Dillustrates an example coordinate system 440 according to embodiments ofthe present disclosure. FIG. 4E illustrates an example diagram 450 of anelectronic device determining that an external electronic device iswithin an azimuth FoV and an elevation FoV according to embodiments ofthe present disclosure.

The electronic device 402 of FIGS. 4A, 4C, 4D, and 4E, the target device410 a of FIGS. 4A, 4D and 4E, and the target device 410 b of FIG. 4A canbe any one of the client devices 106-114 and can include internalcomponents similar to that of electronic device 200 of FIG. 2 and theelectronic device 301 of FIG. 3 . For example, the target device 410 aand the target device 410 b (collectively target device 410) can be aphone (such as the mobile device 108) or a tag attached to a certainobject. In certain embodiments, the electronic device 402 is identifiesthe location of a target device 410 with respect to some FoV of theelectronic device 402, such as the FoV 408 a, as shown in FIG. 4A. Inother embodiments, a remote server, such as the server 104 if FIG. 1 orthe server 308 of FIG. 3 , receives information from the electronicdevice 402 and determines whether a target device 410 is within a FoV ofthe electronic device 402, such as the FoV 408 a. The electronic device402, the target device 410 can be any wireless-enabled device such asthe mobile device 108, a smartphone, a smart watch, a smart tag, atablet computer 114, a laptop computer 112, a smart thermostat, awireless-enabled camera, a smart TV, a wireless-enabled speaker, awireless-enabled power socket, and the like. Based on whether a targetdevice is within the FoV of the electronic device can be used to help auser finding a lost personal item in a nearby area, displayingcontextual menu around the electronic device 402 seen through an ARapplication. The determination of whether the target device 410 a or thetarget device 410 b is within the field of view of the electronic device402 can be performed by any one of the client devices 106-114, theserver 104 of FIG. 1 , The any one of the electronic devices 301, 302,304 of FIG. 3 , the server 308 of FIG. 3 , or the electronic device 402.

In certain embodiments, the electronic device 402, the target device 410a, and the target device 410 b can include a transceiver, such as themeasuring transceiver 270 of FIG. 2 , a UWB transceiver, or the like.Any other suitable transceiver, receiver, or transmitter may be used.Range and AoA information is obtained based on the exchange of signalsbetween the electronic device 402, the target device 410 a, and thetarget device 410 b.

As shown in FIG. 4A, the determination of whether an external electronicdevice (such as either of the target devices 410 a or 410 b) is within aFoV of another electronic device (such as the electronic device 402) isbased on the size and shape of a FoV. A portion of the environmentaround the electronic device 402 is illustrated as FoV 408 a, whileanother portion of the environment around the electronic device 402 isillustrated as outside FoV 408 b. The boundary 404 represents anapproximate boundary between the FoV 408 a and outside the FoV 408 b.The boresight 406 is the center of the FoV 408 a. The boresight 406 canbe the axis of maximum gain (such as maximum radiated power) of anantenna (e.g., a directional antenna) of the electronic device 402. Insome instances, the axis of maximum gain coincides with the axis ofsymmetry of the antenna of the electronic device 402. For example, foraxial-fed dish antennas, the antenna boresight is the axis of symmetryof the parabolic dish, and the antenna radiation pattern (the main lobe)is symmetrical about the boresight axis. Most boresight axes are fixedby their shape and cannot be changed. However, in some implementations,the electronic device 402 includes one or more phased array antennasthat can electronically steer a beam, change the angle of the boresight406 by shifting the relative phase of the radio waves emitted bydifferent antenna elements, radiate beams in multiple directions, andthe like.

The FoV of an electronic device (such as the FoV 408 a of the electronicdevice 402 of FIG. 4A) is a range of angles around the boresight 406,within which the target device (such as the target devices 410 a and 410b) can be defined as being present based on UWB measurements or othermeasurements. The size and shape of a FoV can vary based onenvironmental conditions and the hardware of the electronic deviceitself.

A target device is considered to be within a FoV when it is withinpredefined range (distance) from the electronic device. Range representsthe distance an external device is from a primary electronic device. Inaddition to range (distance), angle features are used to identify alocation of an external electronic device. Angle features, such as AoA,indicates a relative angle that the external device is from the targetdevice (or external electronic device). AoA features are theangle-of-arrival measurements of the second device with respect to thefirst device, and available when the first device has multiple UWBantennas. For a pair of antennas, the phase difference of the signalcoming to each antenna from the second device can be measured, and thenbe used to determine the AoA of the second device.

As illustrated in the diagram 420 of FIG. 4B, the phase difference amongantennas is used to extract the AoA information from UWB measurements.For example, AoA is calculated based on the phase difference of betweenthe two antennas, that of RX1 and RX2. Since the distance between thetwo antennas is fixed, the AoA of a received signal can be identifiedusing the measured phase difference between two antennas.

In certain embodiments, an electronic device can be equipped with morethan more pairs of antennas. A single antenna pair can measure AoA withrespect to one plane. Two or more antenna pairs can measure AoA withrespect to multiple planes. For example, two antenna pairs can measureAoA in both azimuth and elevation angles. For instance, one pair ofantennas is placed in the horizontal direction to measure the angle ofarrival in the horizontal plane, (denoted as the azimuth angle), whilethe other pair of antennas is placed in the vertical direction tomeasure the angle of arrival in the vertical plane (denoted as theelevation angle). This is illustrated in the diagram 430 of FIG. 4C.

FIG. 4C illustrates antenna orientations of an electronic device 402that includes multiple antennas for identifying the azimuth AoA and theelevation AoA. That is, antenna 432 a and antenna 432 b are positionedin the vertical direction for measuring AoA in the elevation direction,while the antenna 432 b and antenna 432 c are positioned in thehorizontal direction for measuring AoA in the azimuth direction. Forexample, a signal received from antenna 432 a and antenna 432 b can beused to generate AoA measurements in elevation. Similarly, a signalreceived from antenna 432 b and antenna 432 c can be used to generateAoA measurements in azimuth.

In certain embodiments, an antenna can be used in multiple antenna pairs(such as the antenna 432 b of FIG. 4C). In other embodiments, eachantenna pair can include separate antennas. The locations of theantennas as shown in the diagram 430 are for example and other antennaplacements, locations, and orientations are possible.

As shown in FIG. 4D, the coordinate system 440 can be used to find thedistance and the relative angle that the target device 410 a is from theelectronic device 402. The distance and the relative angle between thetarget device 410 a and the electronic device 402 correspond to therange and AoA measurements when the target device 410 a is within theFoV of the electronic device 402. The coordinate system 440 illustratesthe azimuth angle and the elevation angle between the two devices. Asillustrated, the azimuth angle is the horizontal angle between theelectronic device 402 and the target device 410 a. Similarly, theelevation angle is the vertical angle between the electronic device 402and the target device 410 a. The coordinate system 440 illustrates therange, r, (distance) between the electronic device 402 and the targetdevice 410 a.

As shown in FIG. 4E, the diagram 450 includes an electronic device 402and a target device 410 a. The electronic device 402 can determinewhether the target device 410 a is within its 3D FoV. A target device isin a 3D FoV when it is within a FoV in both azimuth and elevation. Forexample, the electronic device 402 can include a measuring transceiver(similar to the measuring transceiver 270 of FIG. 2 , a UWB sensor, orthe like) with antenna pairs oriented in a similar manner to asillustrated in FIG. 4C. Measurements obtained from a UWB sensor with twoantenna pairs can include range (distance), AoA elevation 432, and AoAazimuth 434.

Since FoV can be any limit of angles in azimuth and elevation withinwhich the target can be defined as identifiable or present. The FoVlimit in azimuth and elevation may or may not be the same. In certainembodiments, if (i) there is a direct line of sight (LoS) between theelectronic device 402 and a target (such as the target device 410 a or410 b), and (ii) range and AoA measurements are good, then to identifythe presence of target as in FoV or out of FoV can be performed based onAoA measurements in azimuth and elevation. However, many times, themeasurements are corrupted by multipath and NLoS scenarios.Non-isotropic antenna radiation patterns can also result in low qualityof AoA measurements. For example, when the signal received from thedirect path between the target device (such as the target device 410 b)and the device is weak, it is possible that the signal received for thereflected path, based on the environment, can be strong enough to beused for generating the range and AoA measurements. The generated rangeand AoA measurements which are based on a reflected signal would givefalse results of where the target is. For example, the target device 410b can transmit signals to the electronic device 402. If the electronicdevice 402 uses a reflected signal (instead of a direct signal) theelectronic device 402 can incorrectly determine that the target device410 b is located within the FoV 408 a instead of its actual locationwhich is outside the FoV 408 b.

Therefore, embodiments of the present disclosure address problems fordetermining whether the target device is in the FoV of the electronicdevice when the UWB measurements between them may not be very accurate.Embodiments of the present disclosure describe methods for identifyingwhether the target device is within the 3D FoV of the electronic device402 (such as the target device 410 a) or whether a target device isoutside the FoV of the electronic device 402 (such as the target device410 b).

FIGS. 5A, 5B, and 5C illustrate signal processing diagrams 500 a, 500 b,and 500 c, respectively, for field of view determination according toembodiments of the present disclosure. In certain embodiments, thesignal processing diagrams 500 a, 500 b, and 500 c can be performed byany one of the client devices 106-114 of FIG. 1 , any of the electronicdevices 301, 302 and 304 of FIG. 3 , the server 104 of FIG. 1 , theserver 308 of FIG. 3 , and can include internal components similar tothat of electronic device 200 of FIG. 2 and the electronic device 301 ofFIG. 3 .

As discussed above, post processing can be performed to improve thequality of measurements received from transceivers and output a FoVdecision regarding the target device along with smoothed range and AoA.FIGS. 5A, 5B, and 5C describe various signal processing diagrams forimproving the quality of measurements received from transceivers anddetermining whether a target device is within the FoV of the electronicdevice.

As illustrated in FIG. 5A, the signal processing diagram 500 a includesa 3D FoV classifier 510, a motion detection engine 520, and a trackingfilter operation 530. In certain embodiments, if the electronic device(such as the electronic device 402) does not include a motion sensor(such as when the electronic device 200 does not include a sensor 265),then the motion detection engine 520 can be omitted such as illustratedby the signal processing diagram 500 b of FIG. 5B. The motion sensor cangyroscope, accelerometer and magnetometer (inertial measurement unit),and the like. The motion detection engine 520 can cause the trackingfilter operation 530 to reset, upon detecting a motion that is largerthan a threshold.

In certain embodiments, the electronic device (such as the electronicdevice 402) can include multiple 3D FoV classifiers (including the 3DFoV classifiers). For example, the multiple 3D FoV classifiers can beused depending on certain criteria, such as, range, environment, and thelike. As shown in FIG. 5C, the signal processing diagram 500 c includesa scenario identifier 550. If an electronic device includes multiple FoVclassifiers, then the scenario identifier 550 identifies the scenario(such as short or long range, LoS or NLoS). Then depending on theidentified scenario identified, a particular 3D FoV classifier that istrained on that particular scenario, such as the 3D FoV classifiers 510a, is used to do the prediction. For instance, a different 3D FoVclassifier can be used for different scenarios (e.g., different rangemeasurements between the device and target). The 3D FoV classifier 510 acan be the same or similar to the 3D FoV classifier 510 of FIGS. 5A and5B. The scenario identifier 550 is described in FIG. 13 and itscorresponding description, below.

The signal processing diagrams 500 a, 500 b, and 500 c receive variousinputs, such as measurements 502, features 504, measurement 506, andmeasurement 508. The measurements 502 include range, and AoAmeasurements based on the received signals that are communicated betweenthe electric device and the target device. The AoA measurements includeboth AoA in azimuth (AoA_az) and AoA in elevation (AoA_el). The AoAmeasurements can be in the form of degrees, radians, or any other anglebased metric. The measurement 506 includes acceleration of theelectronic device. Similarly, the measurement 508 includes orientationof the electronic device. The acceleration and orientation can bedetected by a motion sensor (if present). It is noted that the signalprocessing diagram 500 b does not include measurement 506 or measurement508 since the signal processing diagram 500 b corresponds to anelectronic device that does not include a motion sensor or a sensor thatis capable of detecting acceleration.

The features 504 include features (such as UWB features) based on thereceived signals that are communicated between the electric device andthe target device. In certain embodiments, the features 504 are derivedfrom CIR. Example features can include, but are not limited to:signal-to-noise ratio (SNR) of the first peak (in dB, in linear domainor with other relative strength indicator) from the CIR, SNR of thestrongest peak (in dB, in linear domain or with other relative strengthindicator) from the CIR, difference between the SNR of strongest andfirst peak (in dB, in linear domain or with other relative strengthindicator), received signal strength (in dB or dBm), and the timedifference between the first and strongest peak (in nsec, number of timesamples or other time based metrics). FIG. 7 illustrates CIR graphsdepicting the first peak and the strongest peak.

FIG. 6 illustrates an example post processor according to embodiments ofthe present disclosure. The embodiment of the post processor 600 shownin FIG. 6 is for illustration only. Other embodiments could be usedwithout departing from the scope of the present disclosure.

In certain embodiments, a post processor can be used to improve thequality of measurements received from UWB transceivers and output the 3DFoV decision regarding the target along with range and AoA in azimuthand elevation. A 3D FoV classifier 605 is used to perform 3D FoV orout-of-FoV prediction about the target using UWB measurements 610 andfeatures 615. UWB measurements 610 include range, AoA in azimuth denotedby AoA_az, and AoA in elevation denoted by AoA_el. UWB features 615 areextracted from the Channel Impulse Response (CIR).

The 3D FoV classifier 605 receives UWB features 615 and UWB measurements610, namely Range, AoA_az, and AoA_el, information in order to make aFoV prediction 620. Additionally, 3D FoV classifier 605 also receivesthe memory state from the Augmented Memory Module (AMM) 625. Herein,memory refers to a set of encoded parameters and information that arelearned and stored to represent the characteristics of a specificenvironment. The AMM 625 takes in the environmental parameters, and thenidentifies whether the environment has been observed before to issue theappropriate memory state. The AMM 625 receives a memory retrievingrequest 630, a memory update request 635, or both. Based on the memoryretrieving request 630, the memory update request 635, or both, the AMM625 provides a memory 640 information to 3D FoV classifier 605 andreceives new memory 645 information from 3D FoV classifier 605.

The 3D FoV classifier 605 can perform the 3D FoV classification usingdeterministic logic, a classical machine learning classifier, or a deeplearning classifier. In certain embodiments, the classification problemis defined as labeling the target to be in ‘FoV’ or ‘out-of-FoV’ of thedevice based on UWB measurements 610. A target is labeled as FoV if thetarget lies within both azimuth and elevation FoV, otherwise the targetis labeled as ‘out-of-FoV’. In certain embodiments, a Recurrent NeuralNetwork is used to perform classification. In certain embodiments, theclassifiers that can be used include, but are not limited to, K-NearestNeighbors (KNN), Support Vector Machine (SVM), Decision Tree, RandomForest, Neural Network, Convolutional Neural Network (CNN).

The 3D FoV classifier 605 performs data collection and data labeling.Training data can be collected by obtaining multiple measurementsbetween the device and the target in FoV and out-of-FoV in both LOS andNLOS setup. To add variation to the data, measurements can be taken atdifferent ranges between the device and the target up to a maximumusable range. Additionally, the environment of data collection can bevaried, for example, data can be collected in an open space environmentor in a cluttered environment prone to multipath. More variations can beadded by changing the tilting angle of the device and target, and byrotating the target at different angles in azimuth and elevation. Datafor the UWB measurements 610 is collected for all combinations of FoVand out-of-FoV of both azimuth and elevation. All the UWB measurements610 can be labeled as per the application depending on which scenario orsetup is required to be labeled as FoV and which one is supposed to beout-of-FoV. In certain embodiments, the UWB measurements 610 are labeledas out-of-FoV in at least the following scenarios: (1) when the targetlies outside the FoV of both azimuth and elevation; (2) when the targetlies in the FoV of azimuth but outside the FoV of elevation; or (3) whenthe target lies in the FoV of elevation but outside the FoV of azimuth.In some instances, only the UWB measurements 610 corresponding to whenthe target lies within the FoV of both azimuth and elevation are labeledas FoV.

The UWB features 615 from UWB measurements 610 that can be used forclassification include (a) statistics (e.g., mean, variance) on themeasurements themselves (e.g., range, raw AoA measurements), and/or (b)features from the CIR of the wireless channel between the device and thetarget. Variance of some of the UWB features 615, for example varianceof range, AoA, over a certain sliding window carry information usefulfor FoV classification. If the window size is K (typically 3-7 samples),a buffer is maintained that stores previous K measurements of thefeatures over which the variance is calculated and used in the featurevector. Instead of variance, other metrics that can measure the spreadof the features can also be used.

FIG. 7 illustrates example CIR graphs 700 a and 700 b for an initial FoVdetermination according to embodiments of the present disclosure. Incertain embodiments, the CIR graphs 700 a and 700 b can be created byany one of the client devices 106-114 or the server 104 of FIG. 1 andcan include internal components similar to that of electronic device 200of FIG. 2 and the electronic device 301 of FIG. 3 .

The CIR graphs 700 a and 700 b of FIG. 7 represent CIRs from twodifferent antennae of the electronic device depending on the orientationof the device. For example, the CIR graph 700 a represents the CIR fromone antenna of an electronic device and the CIR graph 700 b representsthe CIR from another antenna of the same electronic device. The two CIRscan be denoted as CIR_1 and CIR_2.

In certain embodiments, if the electronic device is in portrait mode(such as illustrated in the diagram 430 of FIG. 4C), then CIR_1 andCIR_2 correspond to the CIRs from antenna 432 b and antenna 432 c (orantenna 432 c and antenna 432 b). Similarly, if the electronic device isin landscape mode, then CIR1 and CIR2 correspond to the CIRs fromantenna 432 a and antenna 432 b (or antenna 432 b and antenna 432 a).The determination of portrait or landscape mode can be obtained from IMUsensors.

The CIR graphs 700 a and 700 b show the signal power vs. tap index of areceived signal. The measurements 502 (such as range, AoA_az, andAoA_el) can be calculated based on the earliest peak with sufficient SNR(relative to floor noise) in the CIR graphs 700 a and 700 b. Thefeatures 504 can also be derived from the CIR graphs 700 a and 700 b.The 3D FoV classifier 510 (and the 3D FoV classifier 510 a) can use oneor more of measurements 502 and the features 504 to classifier whetherthe external electronic device is in FoV or out of the FoV. The CIRfeatures of the features 504 can include: (i) absolute strength of oneor multiple peaks in CIR, normally represented by SNR; (ii) tap indicesof one or multiple peaks in CIR, normally represented by index number;(iii) difference in signal strength among multiple peaks in CIR,normally represented by SNR (iv) time differences between multiple peaksin the CIR; (v) phase relationship among multiple antennas used togenerate the AoA information; and (vi) other features derived from theamplitude and phase around the peaks.

In certain embodiments, various feature vectors can be generated by themeasurements 502 and the features 504. The 3D FoV classifier 510 thenuses the feature vectors from for generating the initial prediction ofwhether the target device is within the FoV of the electronic device.For example, a generated feature vector that includes one or more of themeasurements 502 and the features 504 could be expressed as:Feature Vector=[range, AoA_az, PDoA1, PDoA1Idx, AoA_el, PDoA2, PDoA2Idx,SNRFirst1, SNRMain1, FirstIdx1, MainIdx1, SNRFirst2, SNRMain2,FirstIdx2, MainIdx2, var_aoa_win3, var_aoa_win7]  (1)Feature Vector=[AoA_az, PDoA1, PDoA1Idx, AoA_el, PDoA2, PDoA2Idx,SNRFirst1, SNRMain1, FirstIdx1, MainIdx1, SNRFirst2, SNRMain2,FirstIdx2, MainIdx2, var_aoa_win3, var_aoa_win7, var_aoa_el_win3,var_aoa_el_win7 ToAGap1, ToAGap2]  (2)Feature Vector=[range, AoA_az, PDoA1, PDoA1Idx, AoA_el, PDoA2, PDoA2Idx,SNRFirst1, SNRMain1, FirstIdx1, MainIdx1, SNRFirst2, SNRMain2,FirstIdx2, MainIdx2, var_range_win3, var_aoa_win3, var_aoa_win7,var_aoa_el_win5, var_snrfirst1_win5, var_snrfist2_win5]  (3)Feature Vector=[range, AoA_az, AoA_el, SNRFirst1, SNRMain1, FirstIdx1,MainIdx1, SNRFirst2, SNRMain2, FirstIdx2, MainIdx2, var_aoa_win3,var_aoa_win7]  (4)Feature Vector=[range, AoA_az, AoA_el, SNRFirst1, SNRMain1, FirstIdx1,MainIdx1, SNRFirst2, SNRMain2, FirstIdx2, MainIdx2, var_range_win3,var_aoa_win3, var_aoa_win7, var_aoa_el_win5, var_snrfirst1_win5,var_snrfist2_win5]  (5)Feature Vector=[range, AoA_az, PDoA1, PDoA1Idx, AoA_el, PDoA2, PDoA2Idx,SNRFirst1, SNRMain1, FirstIdx1, MainIdx1, SNRFirst2, SNRMain2,FirstIdx2, MainIdx2, var_aoa_win3, var_aoa_win7, ToAGap1, ToAGap2]  (6)Feature Vector=[range, AoA_az, PDoA1, PDoA1Idx, AoA_el, PDoA2, PDoA2Idx,SNRFirst1, SNRMain1, FirstIdx1, MainIdx1, SNRFirst2, SNRMain2,FirstIdx2, MainIdx2, var_aoa_win3, var_aoa_win7, ToAGap1, ToAGap2,min(SNRMain1-SNRFirst1, SNRMain2-SNRFirst2)]  (7)Here, range, AoA_az, and AoA_el represent the measurements 502. Forexample, range is the measured distance from the electronic device tothe external electronic device. Additionally, AoA_az corresponds tomeasurements from one antenna pair (such as the antenna 432 b and theantenna 432 c of FIG. 4C) and AoA_el corresponds to measurements fromanother antenna pair (such as the antenna 432 a and the antenna 432 b ofFIG. 4C). The features “PDoA1 and “PDoA2” represent the phase differenceof arrival from the two different pairs of antennae. The features“PDoA1Idx” and “PDoA2Idx” represent the index of phase difference ofarrival from the two different pairs of antennae. The features“SNRFirst1” and “SNRFirst2” represent the first peak strength from CIR1and CIR2 (such as the first peak strength 712 or 722 of FIG. 7 ). Thefeatures “FirstIdx1” and “FirstIdx2” represent the index of first peakfrom CIR1 and CIR2. The features “SNRMain1” “SNRMain2” represent thestrongest peak strength from CIR1 and CIR2 (such as the strongest peakstrength 714 or 724 of FIG. 7 ). The features “MainIdx1” and “MainIdx2”represent the index of strongest peak from CIR1 and CIR2. The expression“var_aoa_win3” is a first variance of AoA_az in a window size of 3. Thepurpose of “var_aoa_win3,” is to capture the variance of AoA_az in shorttime duration, other size is also possible. The expression“var_aoa_win7” is a second variance of AoA_az in a window size of 7. Thepurpose of “var_aoa_win7” is to capture the variance of AoA_az in longertime duration. Other window size that is larger compared to the firstvariance of AoA_az is also possible. The features “ToAGap1” and“ToAGap2” are the difference between first peak strength 712 andstrongest peak strength 714 of CIR1 and CIR2, respectively. As mentionedpreviously, the selection of antennae for features obtained from CIR1and CIR2 can depend on the orientation of the device, which can beobtained from the IMU. Other ordering or subset of the features in thefeature vector is possible. The difference between the strongest peakstrength and first peak strength is also used as a feature in thefeature set. Variance of range, AoA_el, SNRFirst1 and SNRFirst2 are alsoinformative features and can be used in the feature set forclassification. Other features such as received signal strengthindicator (RSSI) can be included in the features 504.

The features SNRFirst, SNRMain, and ToAGap correspond to a singleantenna of the electronic device. To generate a 3D FoV prediction, thesemeasurements are obtained from two or more antennae. For example,SNRFirst1 and SNRMain1 are the CIR features obtained from antenna 1,while SNRFirst2 and SNRMain2 are the CIR features obtained from antenna2. Additional features can be included for each additional antennas.

Referring back to FIGS. 5A, 5B, and 5C, the 3D FoV classifiers 510 and510 a (collectively 3D FoV classifiers 510), generate an output 540. Theoutput 540 can be a FoV initial prediction of a presence of the externalelectronic device relative to the FoV of the electronic device based ontemporal patterns of the signal information. The 3D FoV classifier 510performs an initial FoV or out-of-FoV prediction about the target devicebased on the measurements 502 and the features 504 to generate theoutput 540. In certain embodiments, the 3D FoV classifier 510 includesmultiple 3D FoV classifiers. For example, when the scenario identifier550 is include, such as in FIG. 5C, the features 504, which are used bythe scenario identifier 550, are also provided to the selected 3D FoVclassifiers 510 a to generate to generate the output 540 correspondingto the initial prediction.

In certain embodiments, the 3D FoV classifier 510 uses deterministiclogic, a classical machine learning classifier, a deep learningclassifier, or a combination thereof to generate an initial predictionof a presence of the target device relative to a FoV of the electronicdevice (representing the output 540). In certain embodiments, theclassification of the 3D FoV classifier 510 labels the target device asin ‘FoV’ or ‘out-of-FoV’ of the electronic device based on inputsincluding the measurements 502 and the features 504. The label cancorrespond to the output 540. For example, a target device is labeled asin FoV if it lies within both azimuth and elevation FoV, otherwise thetarget device is labeled as ‘out-of-FoV’. In certain embodiments, theclassifier that is used in the 3D FoV classifier 510 include a RecurrentNeural Network (RNN). In other embodiments, the classifier that is usedin the 3D FoV classifier 510 include but not limited to, K-NearestNeighbors (KNN), Support Vector Machine (SVM), Decision Tree, RandomForest, Neural Network, Convolutional Neural Network (CNN), and thelike.

Training data for the classifier of the 3D FoV classifier 510 can becollected by obtaining multiple measurements between the electronicdevice and the target device in FoV and out-of-FoV in both LoS and NLoSscenarios. To add variation to the training data, measurements can betaken at different ranges between the electronic device and the targetdevice up to a maximum usable range. Also, the environment of datacollection can be varied. For example, the training data can becollected in an open space environment or in a cluttered environmentprone to multipath. Additional variations can also be added to thetraining data such as by changing the tilting angle of the electronicdevice, the target device, or both devices. Similarly, the training datacan include further variations such as by rotating the target device atdifferent angles in azimuth, elevation, or both. The measurements can belabeled as per the application depending on which scenario or setup isrequired to be labeled as FoV and which one is supposed to beout-of-FoV. In certain embodiments, the measurements can be labeled asout-of-FoV when: (i) the target lies outside the FoV of both azimuth andelevation; (ii) when the target lies in the FoV of azimuth but outsidethe FoV of elevation; or (iii) when the target lies in the FoV ofelevation but outside the FoV of azimuth. In certain embodiments, onlythe measurements corresponding to when the target lies within the FoV ofboth azimuth and elevation are labeled as FoV.

As discussed above, the inputs to the 3D FoV classifier can come fromUWB signal information including: (i) statistics (e.g., mean, variance)on the measurements 502 (e.g., range, raw AoA measurements); (ii) thefeatures 504 from the CIR of the wireless channel between the device andthe target, or a combination thereof.

Variance of some of the features (such as variance of range, variance ofthe AoA), over a certain sliding window also provide information that isuseful for the 3D FoV classifier 510. For example, if the window size isK (such as three to seven samples), a buffer is maintained that storesprevious K measurements of the features over which the variance iscalculated and used in the feature vector. Instead of variance, othermetrics that can measure the spread of the features can also be used.

There are several ways in which the inputs (including the measurements502 and the features 504) are used by the 3D FoV classifier 510 toidentify (predict) when the target is in FoV (representing the output540). For example, when the direct signal path between the device andthe target exists (line of sight, LoS), or in FoV, SNRFirst and SNRMainare close and ToAGap is near-zero. In contrast, in NLoS or out-of-FoVscenarios, the first peak strength 712, representing the direct signalpath, is likely to be of a lower magnitude and far from the main peakstrength 714, which represents the reflected signal path. Therefore, inthe NLoS or out-of-FoV scenario SNRFirst is likely smaller than SNRMainand ToaGap is likely to be large. In the cases when the signal qualityis bad, the first peak strength 712 the strongest peak strength 714 aresusceptible to drifting and likely to have smaller magnitude, thus thedifference between SNRFirst and SNRMain, as well as the ToaGap are goodindicators of whether the target device is in the FoV of the electronicdevice.

Additional features that can be included in the features 504 and provideuseful information for classifying FoV include received signal strengthindicator (RSSI), CIR features from different receiver antennas (AoA isnormally estimated based on the phase difference from multiple antennas)including, but not limited to, SNRFirst, SNRMain and ToAGap. The antennafrom which each of those features is used depends on the correspondinghardware characteristics as suitable for classification.

The features and schemes discussed above, such as the feature vectorsdescribed in Equations (1)-(7), provide advantageous technical effects.Specifically, these features vary significantly when the target isinside FoV and outside FoV, as well as when the target is inside LoS andoutside LoS. Therefore, these features are advantageous in determining astable and accurate decision boundary between FoV and out-of-FoVscenarios, and between LoS and NLoS scenarios.

In certain embodiments, if the electronic device is equipped with onlyone antenna or is operating with one antenna, that is in case of 2Dclassification, the AoA cannot be measured at the device. But the devicecan use the aforementioned features that do not involve multipleantennas, and without the AoA, to perform FoV detection.

In certain embodiments, the 3D FoV classifier 510 includes a long-shortterm memory (LSTM) network. The 3D FoV classifier 510 can use an LSTMnetwork to classify the target device as being in 3D FoV orout-of-3D-FoV (the output 540) using various features vectors, such asany of the feature vectors described in Equations (1)-(7), above. AnLSTM network is a type of RNN that can learn long term dependences. Forexample, LSTM networks can be designed to capture pattern inputs'temporal domain. Stated differently, LSTM captures the temporal patternsin the sequence, and the AoA at a particular sample depends on somerecent past measurements (and not on the measurements in the distantpast). To do so, an LSTM network stores information in two differentembedded vectors, that of (i) a cell state representing long-termmemory, and (ii) a hidden state representing short term memory. FIGS.8A, 8B, and 8C below, describe an LSTM network as used by the 3D FoVclassifier 510.

In certain embodiments, the 3D FoV classifier 510 can be a multi-stageclassifier. If the classifier such as the LSTM does not havesatisfactory performance in some challenging LoS or NLoS scenarios, amore directed classifier based on additional manual logic can be addedto create an ensemble of classifiers and correct the decision of thefirst classifier. The 3D FoV classifier 510 can derive information aboutwhether the target device is in FoV based on variances of the features504. For instance, certain features do not vary or smoothly vary whenthe target is in FoV, while fluctuations in these features increase whenthe target device is out-of-FoV. This information about the spread ofthe features can be used to correct the decision of the firstclassifier, such as the LSTM.

For example, if the variance of AoA_az in a certain window size liesabove a certain threshold or if there is any measurement loss, then theoutput of 3D FoV classifier 510 can directly set to out-of-FoV withoutgetting an inference from the first classifier, such as the LSTM. Foranother example, if (i) raw AoA_az or raw AoA_el lie outside theirrespective FoV ranges and (ii) the output of the first classifier, suchas the LSTM is in FoV, then the 3D FoV classifier 510 changes the FoVprediction to out-of-FoV as the output 540. For another example, if (i)the output of the first classifier, such as the LSTM, is FoV, and (ii)ToAGap is above a threshold or SNRMain-SNRFirst is above itscorresponding threshold, then the 3D FoV classifier 510 changes the FoVprediction to out-of-FoV, as the output 540. For yet another example, if(i) the output of the first classifier, such as the LSTM, is FoV, and(ii) variance of SNRFirst is above a threshold or variance of range isabove the corresponding threshold, then the 3D FoV classifier 510changes the FoV prediction to out-of-FoV, as the output 540.

The motion detection engine 520 of FIGS. 5A and 5C determines whethermotion of the electronic device that exceeds a threshold is detected.When the motion detection engine 520 determines that motion exceeded athreshold, then the motion detection engine 520 can initiate a reset toa tracking filter of the tracking filter operation 530. For example, themotion detection engine 520 monitors measurements from a motion sensor(such as one or more of gyroscope, accelerometer, magnetometer, inertialmeasurement unit, and the like) of the electronic device. The motiondetection engine 520 can initiate a reset operation when a detectedmotion exceeds a threshold. For example, a sudden motion can cause thetracking filter operation 530 to drift which takes time for the trackingfilter operation 530 to converge again. Therefore, when a detectedmotion exceeds a threshold, the motion detection engine 520 can initiatea reset operation to reset the tracking filter of the tracking filteroperation 530.

In certain embodiments (as discussed above), the motion detection engine520 is omitted such as when the electronic device lacks a motion sensor,such as illustrated in the signal processing diagram 500 b of FIG. 5B.

The tracking filter operation 530 of FIGS. 5A, 5B, and 5C, uses one ormore tracking filters to smooth the range and AoA measurements via themeasurements 502. Additionally, if the electronic device is equippedwith a motion sensor (such as a motion sensor that is included in thesensor module 376 of FIG. 3 or a motion sensor that is included in thesensor 265 of FIG. 2 ), information about the motion and orientationchange of the electronic device from this sensor can be used in thetracking filter operation 530 to further improve the quality of rangeand AoA measurements.

In certain embodiments, more than one tracking filter can be used whereeach tracking with a different hypothesis. Example tracking filtersinclude a Kalman filter, an extended Kalman filter, a particle filter,and the like. The tracking filter operation 530 generates output 542.The output 542 can include smoothed range (in meters, centimeters orother distance based metrics). The output 542 can also include thesmoothed AoA_el and the smoothed AoA_az (in degrees, radians or otherangle based metrics).

FIGS. 8A, 8B, and 8C illustrate an example classifier for determiningwhether an external electronic device is in the FoV of an electronicdevice according to embodiments of the present disclosure. Moreparticularly, FIG. 8A illustrates a diagram 800 describing unrollingLSTM forward pass according to embodiments of the present disclosure.FIG. 8B illustrates diagram 820 describing a LSTM cell of the diagram800 of FIG. 8A according to embodiments of the present disclosure. FIG.8C illustrates an example network architecture 830 for 3D FoVclassification according to embodiments of the present disclosure. Theembodiments of FIGS. 8A, 8B, and 8C can be included within or associatedwith the 3D FoV classifier 510 and of FIGS. 5A and 5B and the 3D FoVclassifier 510 a of FIG. 5C.

LSTM is a special type of RNN that is designed specifically to capturethe pattern from the input's temporal domain. It has the advantageouscapability to deal with long sequence and store information in twoembedded vectors. The cell state represents the long-term memory, andthe hidden state represents the short-term memory. Unrolling an LSTMforward pass will result in a chain of repeating cells (such as the LSTMcell 810) connected recurrently as shown in the diagram 800 of FIG. 8A.

At one particular time step, there are three inputs to a given LSTM cell(such as the LSTM cell 810), including the current time step inputx_(t), the hidden state vector from the previous time step unit h_(t-1)and the cell state of the previous time step C_(t-1). In turn, theoutputs of the LSTM cell include the current time step hidden stateh_(t) and the current time step cell state C_(t).

A LSTM cell 810 is illustrated in the diagram 820 of FIG. 8B. Each cellcontains three layers or gates such as a first gate 822, a second gate824, and a third gate 826. The first gate 822 is a forget gate layer,which uses a sigmoid function as activation to control the flow of oldmemory. Based on the current input and previous cell's output, thislayer returns a number f_(t) between 0 and 1 for each element of oldmemory state. An output value f_(t) that is close to 0 indicates thatelement of old memory should be removed, while an output value f_(t)that is close to 1 indicates that element should be retained. Equation(8) describes calculating the output value f_(t).f _(t)=σ(W _(xf) x _(t) +W _(hf) h _(t-1) +b _(f))  (8)Here the weights, W_(xf) and W_(hf), and bias b_(f) are trainableparameters that are obtained during the training process.

The second gate 824 is the new memory gate. The new memory gate controlswhat new information should be added to the memory state. The sigmoidlayer (having an output denoted in FIG. 8B as i_(t)) decides which newinformation should be added or ignored, while tanh layer creates avector {tilde over (C)}_(t) for the new candidate values. The outputs ofthese two layers are element wise multiplied and added to the old memoryto produce a value C_(t). The values relevant to the second gatecalculated as described in Equation (9), Equations (10), and Equations(11).i _(t)=σ(W _(xi) x _(t) +W _(hi) h _(t-1) +b _(i))  (9){tilde over (C)} _(t)=tanh(W _(xc) x _(t) +W _(hc) h _(t-1) +b_(c))  (10)C _(t) =f _(t) ·C _(t-1) +i _(t) ·{tilde over (C)} _(t)  (11)Here, the weights, W_(xi), W_(hi), W_(xc) and W_(hc), and bias b_(i) andb_(c) are trainable parameters that are obtained during the trainingprocess.

The third gate 826 is the output gate. The output gate decides theoutput of the cell and depends on the current memory state, previousmemory state and current input as described in Equation (12) andEquation (13).o _(t)=σ(W _(xo) x _(t) +W _(ho) h _(t-1) +b _(o))  (12)h _(t) =o _(t)·tanh(C _(t))  (13)Here, the weights, W_(xo) and W_(ho), and bias b_(o) are trainableparameters that are obtained during the training process.

The network architecture 830 of FIG. 8C describes the overall process ofthe 3D FoV classifier 510 using LSTM for performing 3D FoVclassification. The network architecture 830 includes an input 832, anLSTM layer 834 (similar to the diagram 800 of FIG. 8A), a dense layer836 and an output 838.

The input 832 for the LSTM is prepared by generating a feature vector(such as the feature vectors described in Equations (1)-(7), above) foreach sample of the sequence. Each sample feature vector is 1×M (where Mis the length of the feature vector) and N such samples in a sequenceare input to the LSTM.

Using the feature vector of Equation (1), feature vector length M=17(due to there being 17 elements in the feature vector of Equation (1))and sequence length N is variable depending on how long the data iscaptured. The output of the LSTM layer 834 is fed into a dense layer836. In the dense layer 836 every neuron in a given layer is connectedto all neurons in the preceding layer, which outputs a probability valuefor FoV/out-of-FoV, which is then thresholded to decide whether thetarget is in FoV or out-of-FoV, which is the output 838.

LSTM captures the temporal patterns in the sequence, and the angle ofarrival at a particular sample depends on some recent past measurements(and not on the measurements in the distant past). If the training datasequences are too long, they are broken down into smaller overlappingsequences so that LSTM does not keep a memory of samples in the distantpast. For example, if the sequence length of 500 with an overlap of 200is used. Hence, N in FIG. 8C is set to 500. It is noted that any amountof overlap can be chosen.

In certain embodiments, measurements can be lost. During training, lostmeasurements are ignored and the remaining measurements are treated as acontinuous sequence. During inference, lost measurements are labeled asout-of-FoV. Alternatively during inference, zero imputation can be donefor lost measurements where all the features in lost measurements can beassigned a zero value and a new feature is added in the feature setwhich indicates if the measurements were lost or not. Example value ofthis feature can be 0 for not lost measurements and 1 for all lostmeasurements.

In other embodiments, other architectures involving LSTM, such asmulti-layer perceptron (MLP), convolutional neural network (CNN), andthe like can be used. In yet other embodiments, to perform 2Dclassification (such as a classification in only azimuth or onlyelevation), a similar architecture as described above using LSTM ofFIGS. 8A-8C with features from a single appropriate antenna pair can beused.

In some instances, the 3D FoV classifier 510 can be a multi-stageclassifier. For example, if the 3D FoV classifier 510 described abovedoes not have satisfactory performance in some challenging LOS or NLOSscenarios, a more directed classifier based on additional manual logiccan be added to create an ensemble of classifiers and correct thedecision of the first classifier. Variance of the features provideimportant information about the target being in FoV since these featuresdo not vary much or vary smoothly when the target is in FoV; however,the fluctuations in these features increase in out-of-FoV. Thisinformation about the spread of the features can be used to correct thedecision of the classifier. In the first embodiment, if the variance ofAoA_az in a certain window size lies above a certain threshold or ifthere is any measurement loss, the output of classification is directlyset to out-of-FoV without getting an inference from the 3D FoVclassifier 510. Also, if raw AoA_az or raw AoA_el lie outside theirrespective FoV ranges and the output of 3D classifier is FoV, it isoverwritten to out-of-FoV.

In certain embodiments, if the output of the 3D FoV classifier 510 isFoV, but ToAGap is above a threshold or SNRMain-SNRFirst is above itscorresponding threshold, the output of the classifier is corrected toout-of-FoV. In another embodiment, if the output of the 3D classifier isFoV, but variance of SNRFirst is above a threshold or variance of rangeis above the corresponding threshold, the output of the classifier iscorrected to out-of-FoV.

FIG. 9 illustrates an augmented memory module architecture according toembodiments of the present disclosure. The embodiment of the AMM 900shown in FIG. 9 is for illustration only. Other embodiments could beused without departing from the scope of the present disclosure.

The AMM 900 is used to enhance the overall performance and theadaptability of the entire system by taking advantage of theenvironments' parameters that the system has already observed. Theseenvironmental parameters are encoded into a memory vector that can bestored and reused appropriately later in order to avoid a blank stateevery time the system encounters an environment that it has seen before.The primary functions of the modules are to process, store, and fetchmemory collected from one or more collections of observed environments.The AMM 900 includes: a Memory Storage 905, the Memory Processor 910,and the Memory Retriever 915.

The AMM 900 is configured to increase the performance of the 3D FOVclassifier 510 especially in the beginning duration of the encounterwith a familiar environment to detect the target object. Additionally,the mechanism in the Memory Processor 910 also allows the AMM 900 toupdate its memories if there is a change in the environment. Therefore,the adaptability of the system is increased and will be more effectivecomparison to a static system where there is no memory to be utilized.

Memory refers to a set of encoded information generated from the inputswhich have been observed and learned so far. Specifically, from theinput, crucial information is extracted, encoded into a parameterizedform, via different mechanisms and stored as memory. The encodedinformation in the memory contains valuable information from pastexperience and highly likely to contain information related to theenvironment. In this sense, memory is often in a tangible form such as atensor with multiple values. In certain embodiments, the memory can bethe long-term memory cell state extracted from the LSTM 3D FOVclassifier 510. Specifically, the memory state is a tensor of shape(D*l, H) containing real values that represent information of theprevious hidden states and the past inputs where D=2 if the LSTM is abidirectional LSTM (sequences are processed in both backward and forwarddirection) and D=1 otherwise. Additionally, l is the number of LSTMcells and H is the hidden size of the LSTM. In the case where the FoVclassifier is implemented using traditional RNN or Gated Recurrent Unit(GRU), the memory state is the hidden state.

FIG. 10 illustrates an example memory state diagram according toembodiments of the present disclosure. The embodiment of the memorystate diagram 1000 shown in FIG. 10 is for illustration only. Otherembodiments could be used without departing from the scope of thepresent disclosure.

In a first example the memory state diagram 1000, assuming a case wherethe RNN is bidirectional, the first l rows illustrate the previoushidden states and the past inputs for the forward direction of the lLSTM cells or RNN units (e.g., first row shows previous hidden statesand the past inputs for the forward direction of the 1^(st) LSTM cell orRNN unit; second row shows previous hidden states and the past inputsfor the forward direction of the 2^(st) LSTM cell or RNN unit; etc.).Furthermore, in such a case, the next l rows illustrate the previoushidden states and the past inputs for the backward direction of the lLSTM cells or RNN units (e.g., (e.g., (l+1)^(th) row shows previoushidden states and the past inputs for the backward direction of the1^(st) LSTM cell or RNN unit; (l+2)^(th) row shows previous hiddenstates and the past inputs for the backward direction of the 2^(nd) LSTMcell or RNN unit; etc.).

In another example of the memory state diagram 1000, assuming a casewhere the RNN is not bidirectional, the l rows illustrate the previoushidden states and the past inputs for the forward direction of the lLSTM cells or RNN units (e.g., first row shows previous hidden statesand the past inputs for the 1^(st) LSTM cell or RNN unit; second rowshows previous hidden states and the past inputs for the 2^(st) LSTMcell or RNN unit; etc.).

The memory storage (MS) 905 is a data table that is used to store thememory vector extracted from each environment 920 In one embodiment, thememory storage can be implemented using a hash table so that accessingthe specific memory can be quick. When the system encounters anenvironment which has been seen before, the memory corresponding to thatenvironment can be retrieved for usage. Tagging an environment is also afunction that needs to be carefully defined so that the environment andthe memory can be associated appropriately.

Different embodiments of the environmental tagging mechanism arepossible. In certain embodiments, the environment can be tagged usingunique identification such as global positioning system (GPS)geolocation. In another embodiment, the environment tagging is done byusing the service set identifier (S SID) of the available Wi-Fi networkat that location. In another embodiment, the environment tagging is doneby comparing the UWB or BLUETOOTH ID of surrounding stationary devices(TV, refrigerator, etc.).

The memory processor 910 writes new memories to the memory storage 905as further illustrated herein below with respect to FIG. 11 . Inoperation 1105, the memory processor 910 determines whether the memoryrelates to a new environment. In operation 1110, the memory processor910 receives new memories if the memories come from a previouslyobserved environment 925. The memory processor 910 updates 930 thememories using Equation 14:m _(e) ←m _(e)+μ*(nm _(e) −m _(e))  (14)

In Equation 14: m_(e) is the memory correspondent to environment estored in memory storage M; nm_(e) is the new memory generated by theFoV classifier from environment e; and μ is the updated rate rangingfrom 0 to 1. In operation 1115, when the environment is new 935, thememory processor 910 inserts 940 the new memory into the memory storage905 M.

The memory retriever 915 retrieves memories from the memory storage 905as further illustrated herein below with respect to FIG. 12 . Inoperation 1205, in response to a memory retrieving request 950, thememory retriever 915 determines whether the memory relates to a newenvironment. In operation 1210, when the environment is not new, thememory retriever 915 reads memory from the memory storage 905 andprovides the read memory 945 to the FoV classifier given thatenvironment has been observed before. In operation 1215, when theenvironment has not been observed before, the memory retriever 915provides a generic memory 955 to the FoV classifier. This generic memoryis specifically trained along the main LSTM with the goal that theclassifier can learn a good default initial state. The generic memory isalso a tensor of shape (D*l, H) which was described earlier in thissection. In this case, the tensor contains real values that representinformation of the initial hidden states for the FoV classifier.

In certain embodiments, the utilization of the AMM 900 is as follows.When the 3D FOV system encounters an unknown environment the 3D FOVclassifier 510 starts from a blank state, meaning there is no memory ofthis new environment. This will result in lower performance of theclassifier at the beginning. The performance increases when theclassifier is more familiar with the performance by processing moreinputs gathered from this environment. At this point the currentlong-term memory cell state of the 3D FOV classifier 510 is tagged andstored in the AMM 900 by the memory processor 910. Alternatively, whenthe system encounters a familiar environment, the encoded memorycorresponding to this environment will be retrieved from the AMM 900 bythe memory retriever 915 and subsequently provided to the 3D FOVclassifier 510. This way, the 3D FOV classifier 510 will not have tostart from a blank state and the performance in the beginning of theencounter will be much better. Additionally, the memory dedicated tothis environment will be updated from this encounter using the mechanismin the memory processor 910. This process provides the system some levelof adaptability toward the changes in the environment.

While the flow chart in FIGS. 11 and 12 depict series of sequentialsteps, unless explicitly stated, no inference should be drawn from thatsequence regarding specific order of performance, performance of stepsor portions thereof serially rather than concurrently or in anoverlapping manner, or performance of the steps depicted exclusivelywithout the occurrence of intervening or intermediate steps. The processdepicted in the example depicted is implemented by a transmitter andprocessor circuitry in, for example, a respective UE. Processes 1100 and1200 can be accomplished by, for example, UE 116 or network device innetwork 100.

FIG. 13 illustrates example method of scenario identification, via thescenario identifier 550 of FIG. 5C for selecting a classifier for aninitial FoV determination by the 3D FoV classifier 510 a according toembodiments of the present disclosure. The method 1300 is described asimplemented by any one of the client devices 106-114 of FIG. 1 and caninclude internal components similar to that of electronic device 200 ofFIG. 2 and the electronic device 301 of FIG. 3 . However, the method1300 as shown in FIG. 13 could be used with any other suitableelectronic device and in any suitable system.

As illustrated in the method 1300, the scenario identifier 550 caninclude a classifier that initially labels the scenario to be LoS orNLoS. Then the scenario identifier 550 selects one of multiple 3D FoVclassifiers, such as the 3D FoV classifier 510 a, that is trained inthat particular scenario labels the target to be in FoV or out-of-FoV.That is, as illustrated in the method 1300, the 3D FoV classifier 510 arepresents two different classifiers. The first classifier is forFoV/out-of-FoV detection in LoS scenario (operation 1315) and the secondfor FoV/out-of-FoV detection in NLoS scenario (operation 1320). In otherembodiments the scenario identifier 550 can identify additionalscenarios in addition to (or in alternative of) the LoS or NLoSscenarios. For example, the scenario identifier 550 can identify ascenario based on a distance between the electronic device and thetarget device and based on the distance the scenario identifier 550selects one of multiple 3D FoV classifiers, that is trained in thatparticular scenario.

In operation 1305, the scenario identifier 550 labels the target deviceas either in LoS or NLoS, based on the features 504. In in operation1310, the scenario identifier 550 determines which classifier to selectto perform the FoV determination, based on the label assigned to thetarget device. When the target device is classified as LoS, then inoperation 1315, a 3D FoV classifier, such as the 3D FoV classifier 510a, is selected, when the 3D FoV classifier 510 a is trained for LoSscenarios. The selected classifier of operation 1315 then determineswhether the target device is in FoV or out of the FoV of the electronicdevice. Alternatively, when the target device is classified as NLoS,then in operation 1320, a 3D FoV classifier such as the 3D classifier510 b (not shown), when the 3D classifier 510 b is trained for NLoSscenarios. The selected classifier of operation 1320 then determineswhether the target device is in FoV or out of the FoV of the electronicdevice.

Although FIG. 13 illustrates an example method, various changes may bemade to FIG. 13 . For example, while the method 1300 is shown as aseries of steps, various steps could overlap, occur in parallel, occurin a different order, or occur multiple times. In another example, stepsmay be omitted or replaced by other steps.

FIG. 14 illustrates an example method 1400 for FoV determination basedon new or existing environment according to embodiments of the presentdisclosure. While the flow chart in FIG. 14 depicts a series ofsequential steps, unless explicitly stated, no inference should be drawnfrom that sequence regarding specific order of performance, performanceof steps or portions thereof serially rather than concurrently or in anoverlapping manner, or performance of the steps depicted exclusivelywithout the occurrence of intervening or intermediate steps. The processdepicted in the example depicted is implemented by a transmitter andprocessor circuitry in, for example, a respective UE. Method 1400 can beaccomplished by, for example, UE 116 or a network device in network 100.

In operation 1405, the electronic device obtains tagging informationthat identifies an environment in which a device is located. Thewireless signals can be received by the electronic device via a firstantenna pair and a second antenna pair that are aligned along differentaxes. The AMM 900 can take in the environmental parameters, and thenidentify whether the environment has been observed before to issue theappropriate memory state. The signal information includes channelinformation, range information, a first AoA of the wireless signalsbased on the first antenna pair, and a second AoA of the wirelesssignals based on the second antenna pair. The first AoA can be inazimuth while the second AoA can be in elevation, or vis-versa. Incertain embodiments, the tagging information can include GPS geolocationinformation, an SSID of a Wi-Fi network at a location, or the device IDsof stationary devices in the environment.

The channel information can include features of a first CIR and a secondCIR of wireless communication channels from the first and second antennapairs, respectively. The first CIR and the second CIR can be based onthe wireless signals communicated between the electronic device and theexternal electronic device.

In operation 1410, the electronic device generates encoded informationfrom a memory module based on the tagging information. In certainembodiments, the encoded information is generated based on a neuralnetwork operating on the tagging information. The neural network caninclude a LSTM neural network. In cases where the electronic device haspreviously encountered the environment, the encoded information includesa current long term memory cell state of the 3D FoV classifier 510. Incases where the electronic device has not previously encountered theenvironment, the encoded information includes a generic initial state ofthe 3D FoV classifier 510.

In operation 1415, the electronic device applies the encoded informationto a 3D FoV classifier 510. For example, the electronic device caninitialize the 3D FoV classifier 510 using the encoded information.

In operation 1420, the electronic device determines whether a target isin a FoV of the electronic device based on the 3D FoV classifier 510operating on UWB features and measurements based on, or included in, thesignal information obtained by the electronic device. In certainembodiments, the electronic device further updates the encodedinformation associated with the tagging information after determiningwhether a target is in a FoV of the electronic device.

Although FIG. 14 illustrates an example method, various changes may bemade to FIG. 14 . For example, while the method 1400 is shown as aseries of steps, various steps could overlap, occur in parallel, occurin a different order, or occur multiple times. In another example, stepsmay be omitted or replaced by other steps.

The above flowcharts illustrate example methods that can be implementedin accordance with the principles of the present disclosure and variouschanges could be made to the methods illustrated in the flowchartsherein. For example, while shown as a series of steps, various steps ineach figure could overlap, occur in parallel, occur in a differentorder, or occur multiple times. In another example, steps may be omittedor replaced by other steps.

Although the present disclosure has been described with an exemplaryembodiment, various changes and modifications may be suggested to oneskilled in the art. It is intended that the present disclosure encompasssuch changes and modifications as fall within the scope of the appendedclaims. None of the description in this application should be read asimplying that any particular element, step, or function is an essentialelement that must be included in the claims scope.

What is claimed is:
 1. An electronic device, comprising: a processorconfigured to: obtain signal information based on wireless signalsreceived from a target electronic device via a first antenna pair and asecond antenna pair, wherein the first and second antenna pairs arealigned along different axes and wherein the signal information includeschannel information, range information, a first angle of arrival (AoA)of the wireless signals based on the first antenna pair, and a secondAoA of the wireless signals based on the second antenna pair; obtaintagging information that identifies an environment in which theelectronic device is located; generate encoded information from a memorymodule based on the tagging information; initialize a field of view(FoV) classifier based on the encoded information; and determine whetherthe target electronic device is in a FoV of the electronic device basedon the FoV classifier operating on the signal information.
 2. Theelectronic device of claim 1, wherein: when the electronic device haspreviously encountered the environment, the encoded information includesa current long-term memory cell state of the FoV classifier; or when theelectronic device has not previously encountered the environment, theencoded information includes a generic initial state of the FoVclassifier.
 3. The electronic device of claim 1, wherein the processoris further configured to: update the encoded information associated withthe tagging information.
 4. The electronic device of claim 1, wherein:the tagging information includes a global positioning system (GPS)geolocation information, a service set identifier (SSID) of a Wi-Finetwork at a location, or an ID of a stationary external electronicdevice located in the environment.
 5. The electronic device of claim 1,wherein the processor is further configured to: determine whether theenvironment is a new environment or a previously experiencedenvironment.
 6. The electronic device of claim 1, wherein the FoVclassifier comprises at least one of: an augmented memory module or along-short term memory (LSTM) neural network.
 7. The electronic deviceof claim 4, wherein: the channel information comprises features of afirst channel impulse response (CIR) and a second CIR of wirelesscommunication channels from the first and second antenna pairs,respectively, wherein the first CIR and the second CIR are based on thewireless signals communicated between the electronic device and thetarget electronic device; the processor is further configured togenerate a feature vector based on the range information, the first andthe second AoA, and the features; and wherein the feature vectorcomprises at least two of: the range information, indicating a distancebetween the electronic device and the target electronic device; thefirst AoA; the second AoA; phase difference of arrival from the firstand second antenna pairs; an index of the phase difference of arrivalfrom the first and second antenna pairs; strength of a first peak of thefirst CIR; strength of a strongest peak of the first CIR; an index ofthe first peak from the first CIR; an index of the strongest peak fromthe first CIR; strength of a first peak of the second CIR; strength of astrongest peak of the second CIR; an index of the first peak from thesecond CIR; an index of the strongest peak from the second CIR; avariance of the first AoA over a first time interval; a variance of thefirst AoA over a second time interval that is longer than the first timeinterval; a variance of the second AoA over the first time interval; avariance of the second AoA over the second time interval; a first timedifference between the first peak strength of the first CIR and thestrongest peak strength of the first CIR; or a second time differencebetween the first peak strength of the second CIR and the strongest peakstrength of the second CIR.
 8. A method for operating an electronicdevice, the method comprising: obtaining signal information based onwireless signals received from a target electronic device via a firstantenna pair and a second antenna pair, wherein the first and secondantenna pairs are aligned along different axes and wherein the signalinformation includes channel information, range information, a firstangle of arrival (AoA) of the wireless signals based on the firstantenna pair, and a second AoA of the wireless signals based on thesecond antenna pair; obtaining tagging information that identifies anenvironment in which the electronic device is located; generatingencoded information from a memory module based on the tagginginformation; initializing a field of view (FoV) classifier based on theencoded information; and determining whether the target electronicdevice is in a FoV of the electronic device based on the FoV classifieroperating on the signal information.
 9. The method of claim 8, wherein:when the electronic device has previously encountered the environment,the encoded information includes a current long-term memory cell stateof the FoV classifier; or when the electronic device has not previouslyencountered the environment, the encoded information includes a genericinitial state of the FoV classifier.
 10. The method of claim 8, furthercomprising: updating the encoded information associated with the tagginginformation.
 11. The method of claim 8, wherein: the tagging informationincludes a global positioning system (GPS) geolocation information, aservice set identifier (SSID) of a Wi-Fi network at a location, or an IDof a stationary external electronic device located in the environment.12. The method of claim 8, further comprising: determining whether theenvironment is a new environment or a previously experiencedenvironment.
 13. The method of claim 8, wherein the FoV classifiercomprises at least one of: an augmented memory module or a long-shortterm memory (LSTM) neural network.
 14. The method of claim 8, whereinthe channel information comprises features of a first channel impulseresponse (CIR) and a second CIR of wireless communication channels fromthe first and second antenna pairs, respectively, wherein the first CIRand the second CIR are based on the wireless signals communicatedbetween the electronic device and the target electronic device, themethod further comprising: generating a feature vector based on therange information, the first and the second AoA, and the features,wherein the feature vector comprises at least two of: the rangeinformation, indicating a distance between the electronic device and thetarget electronic device; the first AoA; the second AoA; phasedifference of arrival from the first and second antenna pairs; an indexof the phase difference of arrival from the first and second antennapairs; strength of a first peak of the first CIR; strength of astrongest peak of the first CIR; an index of the first peak from thefirst CIR; an index of the strongest peak from the first CIR; strengthof a first peak of the second CIR; strength of a strongest peak of thesecond CIR; an index of the first peak from the second CIR; an index ofthe strongest peak from the second CIR; a variance of the first AoA overa first time interval; a variance of the first AoA over a second timeinterval that is longer than the first time interval; a variance of thesecond AoA over the first time interval; a variance of the second AoAover the second time interval; a first time difference between the firstpeak strength of the first CIR and the strongest peak strength of thefirst CIR; or a second time difference between the first peak strengthof the second CIR and the strongest peak strength of the second CIR. 15.A non-transitory computer readable medium containing instructions thatwhen executed by a processor of an electronic device, cause theprocessor to: obtain signal information based on wireless signalsreceived from a target electronic device via a first antenna pair and asecond antenna pair, wherein the first and second antenna pairs arealigned along different axes and wherein the signal information includeschannel information, range information, a first angle of arrival (AoA)of the wireless signals based on the first antenna pair, and a secondAoA of the wireless signals based on the second antenna pair; obtaintagging information that identifies an environment in which theelectronic device is located; generate encoded information from a memorymodule based on the tagging information; initialize a field of view(FoV) classifier based on the encoded information; and determine whetherthe target electronic device is in a FoV of the electronic device basedon the FoV classifier operating on the signal information.
 16. Thenon-transitory computer readable medium of claim 15, wherein theinstructions are further configured to cause the processor to: updatethe encoded information associated with the tagging information.
 17. Thenon-transitory computer readable medium of claim 15, wherein: when theelectronic device has previously encountered the environment, theencoded information includes a current long-term memory cell state ofthe FoV classifier; or when the electronic device has not previouslyencountered the environment, the encoded information includes a genericinitial state of the FoV classifier.
 18. The non-transitory computerreadable medium of claim 15, wherein: the tagging information includes aglobal positioning system (GPS) geolocation information, a service setidentifier (SSID) of a Wi-Fi network at a location, or an ID of astationary external electronic device located in the environment. 19.The non-transitory computer readable medium of claim 15, wherein theinstructions are further configured to cause the processor to: determinewhether the environment is a new environment or a previously experiencedenvironment.
 20. The non-transitory computer readable medium of claim18, wherein the FoV classifier comprises at least one of: an augmentedmemory module or a long-short term memory (LSTM) neural network.